One of the most critical challenges in computational fluid dynamics (CFD) and machine learning (ML) is that high-resolution 3D data sets designed specifically for automotive aerodynamics are very difficult to find in the public domain. The resources used are often low fidelity, not to mention the conditions, making it impossible to create scalable and accurate machine learning models. Furthermore, the available data sets for the diversity of geometric variations are limited, severely limiting improvements in aerodynamic design optimization. Filling these gaps is critical to accelerating innovation in predictive aerodynamic tools and design processes for modern road vehicles.
Classical methods for generating aerodynamic data have mainly relied on simplified or low-resolution 3D geometries, which cannot support the requirements of high-performance ML models. For example, datasets like AhmedML, although novel, use grid dimensions of approximately 20 million cells, which is much less than the industry benchmark of over 100 million cells. This limits scalability and makes the relevance of machine learning models for practical applications less significant. Furthermore, existing data sets often suffer from poor geometric diversity and rely on less precise computational fluid dynamics techniques, meaning there is very limited scope to address the complex aerodynamic phenomena encountered in real designs.
Researchers from amazon Web Services, Volcano Platforms Inc., Siemens Energy, and Loughborough University introduced WindsorML to address these limitations. This high-fidelity, open-source CFD data set contains 355 geometric variations of the Windsor body configuration, typical of modern vehicles. Using WMLES containing over 280 million cells, WindsorML delivers exceptional detail and resolution. The data set is composed of various geometric configurations generated with deterministic Halton sampling for comprehensive coverage of aerodynamic scenarios. Advanced CFD methods and GPU-accelerated solvers enable accurate simulation of flow fields, surface pressures and aerodynamic forces, thus setting a new benchmark for high-resolution aerodynamic data sets.
The Volcano ScaLES solver generated the data set using a Cartesian grid with refinement focused on areas of interest, such as boundary layers and wakes. Each simulation captures time-averaged information related to volumetric and surface flow fields, aerodynamic force coefficients, and geometric parameters, all of which are provided in widely accepted open source formats such as `.vtu` and `.stl`. Systematic variation of seven geometric parameters, including clearance and taper angles, produces a wide range of aerodynamic behaviors within a comprehensive data set. The accuracy of this data set is further validated by a grid refinement analysis, ensuring robust and reliable results that agree with experimental benchmarks.
WindsorML demonstrates exceptional performance and versatility, which is validated by its consistency with experimental aerodynamic data. The data set provides detailed information on flow behaviors and force coefficients, including both drag and lift, with a wide range of configurations, underscoring its value for practical applications. Preliminary evaluations based on machine learning models, such as Graph Neural Networks, show great promise for predictive aerodynamic modeling. These models also exhibit good accuracy in aerodynamic coefficient predictions to illustrate the effectiveness of this data set in efficiently training machine learning systems. WindsorML's comprehensive results and high resolution make it an invaluable resource for advancing CFD and ML methodologies in automotive aerodynamics.
By overcoming the limitations of existing data sets, WindsorML offers a transformative resource for the CFD and ML communities. Helps develop scalable yet accurate predictive models for aerodynamic evaluations. With high-fidelity simulations and diverse geometric configurations, it is well prepared to help accelerate innovation in vehicle design and provide a solid foundation for integrating ai into workflows for aerodynamic analysis.
Verify he <a target="_blank" href="https://www.amazon.science/publications/windsorml-high-fidelity-computational-fluid-dynamics-dataset-for-automotive-aerodynamics” target=”_blank” rel=”noreferrer noopener”>Paper. All credit for this research goes to the researchers of this project. Also, don't forget to follow us on <a target="_blank" href="https://twitter.com/Marktechpost”>twitter and join our Telegram channel and LinkedIn Grabove. Don't forget to join our SubReddit over 60,000 ml.
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Aswin AK is a consulting intern at MarkTechPost. He is pursuing his dual degree from the Indian Institute of technology Kharagpur. He is passionate about data science and machine learning, and brings a strong academic background and practical experience solving real-life interdisciplinary challenges.
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